Engineering Software Analytics StudiesSpeaker: Diomidis Spinellis – Athens, Greece
Topic(s): Software Engineering and Programming
AbstractPerforming quantitative software analytics studies can be an immensely rewarding activity for scientists performing empirical research. However, such studies often pose numerous engineering challenges. The researcher must hunt down appropriate data sets, devise bespoke collection and processing tools, and optimize performance to match the size of the collected data. I will discuss principles and strategies that can be used to deal with these problems, and present examples of associated tools and techniques. Some particularly effective strategies associated with data set construction involve recursion, web searching, synthesis, probing, instrumentation, and the nurturing of alliances. On the processing front approaches include the opportunistic scavenging of tool front-ends, the exploratory development of pipelines, as well as the exploitation of tool interoperability, scripting languages, and their rich libraries. The required performance can be obtained through parallelism, stream processing, the judicious use of low-level facilities, and the choice of appropriate samples. I will finish the presentation with an overview of open problems and challenges in software analytics in vertical domains, data analysis, and under-represented stakeholders.
About this LectureNumber of Slides: 138
Duration: 50 minutes
Languages Available: English
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